As network environments become increasingly complex, data management systems are collecting, organizing, storing, and analyzing data reaching big data levels (for example, hundreds of terabytes of data). Today's data management systems implement conventional database architectures, such as relational databases and columnar databases, which are not well suited for processing dynamic big data, particularly big time series data. Conventional database architectures, including specialized time series databases, exhibit either limited data query and data analytic flexibility or limited data insertion and data query rates. Accordingly, data management systems are exploring various database architectures for improving data insertion and data query metrics without sacrificing data query capabilities.